MR imaging findings are a stronger predictor of pathologic response to NACT than clinical assessment, with the greatest advantage observed with the use of volumetric measurement of tumor response early in treatment.

Automated image analysis aims to extract relevant information from contrast-enhanced magnetic resonance images (CE-MRI) of the breast and improve the accuracy and consistency of image interpretation. In this work, we extend the traditional 2D gray-level co-occurrence matrix (GLCM) method to investigate a volumetric texture analysis approach and apply it for the characterization of breast MR lesions. Our database of breast MR images was obtained using a T1-weighted 3D spoiled gradient echo sequence and consists of 121 biopsy-proven lesions (

).q RSNA, 2015 Purpose:To evaluate volumetric magnetic resonance (MR) imaging for predicting recurrence-free survival (RFS) after neoadjuvant chemotherapy (NACT) of breast cancer and to consider its predictive performance relative to pathologic complete response (PCR). Materials and Methods:This HIPAA-compliant prospective multicenter study was approved by institutional review boards with written informed consent. Women with breast tumors 3 cm or larger scheduled for NACT underwent dynamic contrastenhanced MR imaging before treatment (examination 1), after one cycle (examination 2), midtherapy (examination 3), and before surgery (examination 4). Functional tumor volume (FTV), computed from MR images by using enhancement thresholds, and change from baseline (DFTV) were measured after one cycle and before surgery. Association of RFS with FTV was assessed by Cox regression and compared with association of RFS with PCR and residual cancer burden (RCB), while controlling for age, race, and hormone receptor (HR)/ human epidermal growth factor receptor type 2 (HER2) status. Predictive performance of models was evaluated by C statistics. Results:Female patients (n = 162) with FTV and RFS were included. Conclusion:Breast tumor FTV measured by MR imaging is a strong predictor of RFS, even in the presence of PCR and RCB class. Models combining MR imaging, histopathology, and breast cancer subtype demonstrated the strongest predictive performance in this study.q RSNA, 2015

Importance: Improved screening methods for women with dense breasts are needed because of their increased risk of breast cancer and of failed early diagnosis by screening mammography. Objective: To compare the screening performance of abbreviated breast MRI (AB-MR), and digital breast tomosynthesis (DBT) in women with dense breasts. Design, Setting, and Participants: Cross-sectional study with longitudinal follow-up at 48 academic, community hospital, and private practice sites in the US and Germany, conducted between December 2016 and November 2017, that included average-risk women aged 40-75 years with heterogeneously dense or extremely dense breasts undergoing routine screening. Follow up ascertainment of cancer diagnoses was complete through September 12 th , 2019. Exposure: All women underwent screening by both DBT and AB-MR, performed in randomized order and read independently to avoid interpretation bias. Main outcome measures: The primary endpoint was the invasive cancer detection rate. Secondary outcomes included sensitivity, specificity, the additional-imaging-recommendation-rate, and positive predictive value (PPV) of biopsy, using invasive cancer and DCIS to define a positive reference standard. All outcomes are reported at the participant level. Pathology of core or surgical biopsy was the reference standard for cancer detection rate and PPV; interval cancers reported until the next annual screen were included in the reference standard for sensitivity and specificity. Results: Among 1516 enrolled women, 1444 (median age 54, range 40-75) completed both examinations and were included in the analysis. The reference standard was positive for invasive cancer with or without DCIS in 17 women, and for DCIS alone in another 6. No interval cancers were observed during follow-up. AB-MR detected all 17 women with invasive cancer, and 5/6 women with DCIS. DBT detected 7/17 women with invasive cancer, and 2/6 women with DCIS. The invasive-cancer-detection-rate was 11.8 per 1000 women [95% CI 7.4-18.8] for AB-MR versus 4.8 per 1000 women [95% CI 2.4-10.0] for DBT, a difference of 7 per 1000 women [95% CI for the difference 2.2-11.6] (exact McNemar p=0.002). For detection of invasive cancer and Comstock et al.

Identifying the presence of axillary node and internal mammary node metastases in patients with invasive breast cancer is critical for determining prognosis and for deciding on appropriate treatment. Sentinel lymph node biopsy (SLNB) is the definitive method to exclude axillary metastases. Patients with positive SLNB results generally undergo axillary lymph node dissection (ALND). The benefit of preoperative identification of axillary metastases is that it allows the surgeon to proceed directly to ALND and to avoid an unnecessary SLNB and the need for a second surgical procedure involving the axillary nodes. Knowledge of the important anatomic landmarks of the axilla is important in finding and accurately reporting suspicious lymph nodes. The pathologic features of nodal metastases illuminate the imaging appearances of these nodes, as depicted with all modalities. Ultrasonography (US) is the primary imaging modality for evaluating axillary nodes. Morphologic criteria, such as cortical thickening, hilar effacement, and nonhilar cortical blood flow, are more important than size criteria in the identification of metastases. US-guided lymph node sampling, especially with core biopsy, is invaluable in confirming the presence of a metastasis in a suspicious node. Core biopsy has been shown to be equal in safety to fine needle aspiration and has a significantly lower false-negative rate. Magnetic resonance imaging is also useful, with the added benefit of providing a global view of both axillae. Computed tomography and radionuclide imaging play a lesser role in imaging the axilla. Preoperative image-based identification and sampling of abnormal lymph nodes that have a high positive predictive value for metastases is an extremely important component in the management of patients with invasive breast cancer.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast is being used increasingly in the detection and diagnosis of breast cancer as a complementary modality to mammography and sonography. Although the potential diagnostic value of kinetic curves in DCE-MRI is established, the method for generating kinetic curves is not standardized. The inherent reason that curve identification is needed is that the uptake of contrast agent in a breast lesion is often heterogeneous, especially in malignant lesions. It is accepted that manual region of interest selection in 4D breast magnetic resonance (MR) images to generate the kinetic curve is a time-consuming process and suffers from significant inter- and intraobserver variability. We investigated and developed a fuzzy c-means (FCM) clustering-based technique for automatically identifying characteristic kinetic curves from breast lesions in DCE-MRI of the breast. Dynamic contrast-enhanced MR images were obtained using a T1-weighted 3D spoiled gradient echo sequence with Gd-DTPA dose of 0.2 mmol/kg and temporal resolution of 69 s. FCM clustering was applied to automatically partition the signal-time curves in a segmented 3D breast lesion into a number of classes (i.e., prototypic curves). The prototypic curve with the highest initial enhancement was selected as the representative characteristic kinetic curve (CKC) of the lesion. Four features were then extracted from each characteristic kinetic curve to depict the maximum contrast enhancement, time to peak, uptake rate, and washout rate of the lesion kinetics. The performance of the kinetic features in the task of distinguishing between benign and malignant lesions was assessed by receiver operating characteristic analysis. With a database of 121 breast lesions (77 malignant and 44 benign cases), the classification performance of the FCM-identified CKCs was found to be better than that from the curves obtained by averaging over the entire lesion and similar to kinetic curves generated from regions drawn within the lesion by a radiologist experienced in breast MRI.

Purpose:To assess the performance of computer-extracted dynamic contrast material-enhanced (DCE) magnetic resonance (MR) imaging kinetic and morphologic features in the differentiation of invasive versus noninvasive breast lesions and metastatic versus nonmetastatic breast lesions. Materials and Methods:In this institutional review board-approved HIPAA-compliant study, in which the requirement for informed patient consent was waived, breast MR images were retrospectively collected. The images had been obtained with a 1.5-T MR unit by using a gadodiamide-enhanced T1-weighted spoiled gradient-recalled acquisition in the steady state sequence. The breast MR imaging database contained 132 benign, 71 ductal carcinoma in situ (DCIS), and 150 invasive ductal carcinoma (IDC) lesions. Fifty-four IDC lesions were associated with metastasis-positive lymph nodes (LNs), and 64 IDC lesions were associated with negative LNs. Lesion segmentation and extraction of morphologic and kinetic features were automatically performed by a laboratory-developed computer workstation. Features were fi rst selected by using stepwise linear discriminant analysis and then merged by using Bayesian neural networks. Lesion classifi cation performance was assessed with receiver operating characteristic analysis. Results: Conclusion:Computer-aided diagnosis of breast DCE MR imagingdepicted lesions was extended from the task of discriminating between malignant and benign lesions to the prognostic tasks of distinguishing between noninvasive and invasive lesions and discriminating between metastatic and nonmetastatic lesions, yielding MR imaging-based prognostic markers.q RSNA, 2010 Supplemental material: http://radiology.rsna.org/lookup/ suppl

http://radiology.rsnajnls.org/cgi/content/full/245/3/684/DC1.